roc_auc.py

from sklearn import metrics
auc = metrics.roc_auc_score(y_test, y_pred)

false_positive_rate, true_positive_rate, thresolds = metrics.roc_curve(y_test, y_pred)

plt.figure(figsize=(10, 8), dpi=100)
plt.axis('scaled')
plt.xlim([0, 1])
plt.ylim([0, 1])
plt.title("AUC & ROC Curve")
plt.plot(false_positive_rate, true_positive_rate, 'g')
plt.fill_between(false_positive_rate, true_positive_rate, facecolor='lightgreen', alpha=0.7)
plt.text(0.95, 0.05, 'AUC = %0.4f' % auc, ha='right', fontsize=12, weight='bold', color='blue')
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
plt.show()